This invention relates generally to networks of individuals and/or entities and network communities and, more particularly, to systems and methods for determining trust scores or connectivity within or between individuals and/or entities or networks of individuals and/or entities.
The connectivity, or relationships, of an individual or entity within a network community may be used to infer attributes of that individual or entity. For example, an individual or entity's connectivity within a network community may be used to determine the identity of the individual or entity (e.g., used to make decisions about identity claims and authentication), the trustworthiness or reputation of the individual or entity, or the membership, status, and/or influence of that individual or entity in a particular community or subset of a particular community.
An individual or entity's connectivity within a network community, however, is difficult to quantify. For example, network communities may include hundreds, thousands, millions, billions or more members. Each member may possess varying degrees of connectivity information about itself and possibly about other members of the community. Some of this information may be highly credible or objective, while other information may be less credible and subjective. In addition, connectivity information from community members may come in various forms and on various scales, making it difficult to meaningfully compare one member's “trustworthiness” or “competence” and connectivity information with another member's “trustworthiness” or “competence” and connectivity information. Also, many individuals may belong to multiple communities, further complicating the determination of a quantifiable representation of trust and connectivity within a network community. Even if a quantifiable representation of an individual's connectivity is determined, it is often difficult to use this representation in a meaningful way to make real-world decisions about the individual (e.g., whether or not to trust the individual).
Further, it may be useful for these real-world decisions to be made prospectively (i.e., in advance of an anticipated event). Such prospective analysis may be difficult as an individual or entity's connectivity within a network community may change rapidly as the connections between the individual or entity and others in the network community may change quantitatively or qualitatively. This analysis becomes increasingly complex as if applied across multiple communities.